Booking service increases revenue through optimization of advertising costs

Used tools

Objective

Thousands of customers use this service to book flights and hotels every day. Some of
them generate more revenue and make bookings more often, some seldom make
purchases, others do not place orders at all. The main business objective was to break
the paying audience down by their buying characteristics, then identify the most
valuable customers and develop different strategies to boost sales in each segment.

Solution

OWOX recommended that they perform RFM and LTV analysis based on the CRM data in Google BigQuery.

CRM data was first processed in Google BigQuery to determine R (recency), F (frequency),
M (monetary) value and LTV segment ID for each user. During the RFM analysis
users of each segment were divided into three different groups (1 — the most valuable
users, 3 — the least valuable users), which resulted in 27 different groups. In LTV analysis
users were divided into five segments (1 — being customers who bring in the most
revenue, 5 — being customers who bring in the least revenue).

The second step was to upload the data from Google BigQuery to Google Analytics.
The following algorithm was applied:

Professional team

Main goal was to build an analytical system which would be able to automate collection and calculation of key performance indicators. We achieved the following results: — automatic calculation of ROI for paid advertising campaigns on different levels — calculation and analysis of additional KPIs through GA API — possibility to create non-standard reports and flexibility in setting up data collection from the website with GTM So, we obtained more opportunities for business process management